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The 1.6 rule

In ARM we discuss how you can go back and forth between logit and probit models by dividing by 1.6. Or, to put it another way, logistic regression corresponds to a latent-variable model with errors that are approximately normally distributed with mean 0 and standard deviation 1.6. (This is well known, it’s nothing original with our book.) Anyway, John Cook discusses the approximation here.

One Comment

  1. Andrew Hill says:

    Nice link. This has also cropped up in meta-analysis, see Chinn Stat Med 2000, which matches moments.